{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 引包\n",
    "from sklearn import datasets, cross_validation, naive_bayes\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 显示Digit数据\n",
    "def show_digits():\n",
    "    digits = datasets.load_digits()\n",
    "    fig = plt.figure()\n",
    "    #print('images 0 :',digits.data[0])\n",
    "    for i in range(25):\n",
    "        ax = fig.add_subplot(5,5,i+1)\n",
    "        #print(digits.images[i])\n",
    "        ax.imshow(digits.images[i], cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_digits()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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